Metafounders May Reduce Bias in Composite Cattle Genomic Predictions

Metafounders are pseudo-individuals that act as proxies for animals in base populations. When metafounders are used, individuals from different breeds can be related through pedigree, improving the compatibility between genomic and pedigree relationships. The aim of this study was to investigate the use of metafounders and unknown parent groups (UPGs) for the genomic evaluation of a composite beef cattle population. Phenotypes were available for scrotal circumference at 14 months of age (SC14), post weaning gain (PWG), weaning weight (WW), and birth weight (BW). The pedigree included 680,551 animals, of which 1,899 were genotyped for or imputed to around 30,000 single-nucleotide polymorphisms (SNPs). Evaluations were performed based on pedigree (BLUP), pedigree with UPGs (BLUP_UPG), pedigree with metafounders (BLUP_MF), single-step genomic BLUP (ssGBLUP), ssGBLUP with UPGs for genomic and pedigree relationship matrices (ssGBLUP_UPG) or only for the pedigree relationship matrix (ssGBLUP_UPGA), and ssGBLUP with metafounders (ssGBLUP_MF). Each evaluation considered either four or 10 groups that were assigned based on breed of founders and intermediate crosses. To evaluate model performance, we used a validation method based on linear regression statistics to obtain accuracy, stability, dispersion, and bias of (genomic) estimated breeding value [(G)EBV]. Overall, relationships within and among metafounders were stronger in the scenario with 10 metafounders. Accuracy was greater for models with genomic information than for BLUP. Also, the stability of (G)EBVs was greater when genomic information was taken into account. Overall, pedigree-based methods showed lower inflation/deflation (regression coefficients close to 1.0) for SC14, WWM, and BWD traits. The level of inflation/deflation for genomic models was small and trait-dependent. Compared with regular ssGBLUP, ssGBLUP_MF4 displayed regression coefficient closer to one SC14, PWG, WWM, and BWD. Genomic models with metafounders seemed to be slightly more stable than models with UPGs based on higher similarity of results with different numbers of groups. Further, metafounders can help to reduce bias in genomic evaluations of composite beef cattle populations without reducing the stability of GEBVs.

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Comparison of models for missing pedigree in single-step genomic prediction

Journal of Animal Science ◽

10.1093/jas/skab019 ◽

2021 ◽

Vol 99 (2) ◽

Author(s):

Yutaka Masuda ◽

Shogo Tsuruta ◽

Matias Bermann ◽

Heather L Bradford ◽

Ignacy Misztal

Keyword(s):

Density Function ◽

Single Step ◽

Joint Density ◽

Pedigree Information ◽

Relationship Matrix ◽

Genomic Information ◽

Breeding Values ◽

Joint Density Function ◽

Information Updates ◽

Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

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Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population

Frontiers in Genetics ◽

10.3389/fgene.2020.556399 ◽

2020 ◽

Vol 11 ◽

Author(s):

Vinícius Silva Junqueira ◽

Paulo Sávio Lopes ◽

Daniela Lourenco ◽

Fabyano Fonseca e Silva ◽

Fernando Flores Cardoso

Keyword(s):

Beef Cattle ◽

Variance Components ◽

Genetic Relationships ◽

A Priori ◽

Predictive Ability ◽

Single Step ◽

Pedigree Information ◽

Phenotypic Variance ◽

Genomic Information ◽

The Impact

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

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Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo

Frontiers in Genetics ◽

10.3389/fgene.2021.692356 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hossein Mehrban ◽

Masoumeh Naserkheil ◽

Deukhwan Lee ◽

Noelia Ibáñez-Escriche

Keyword(s):

Beef Cattle ◽

Carcass Traits ◽

Single Step ◽

Birth Date ◽

Linear Regression Method ◽

Genomic Information ◽

Muscle Area ◽

Eye Muscle ◽

Correlated Traits ◽

The Impact

There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.

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The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Animals ◽

10.3390/ani10040569 ◽

2020 ◽

Vol 10 (4) ◽

pp. 569

Author(s):

Chen Wei ◽

Hanpeng Luo ◽

Bingru Zhao ◽

Kechuan Tian ◽

Xixia Huang ◽

...

Keyword(s):

Rank Correlation ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Breeding Value ◽

Genomic Information ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Merino Sheep ◽

Best Linear Unbiased ◽

Unbiased Prediction

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

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Application of single-step GBLUP in New Zealand Romney sheep

Animal Production Science ◽

10.1071/an19315 ◽

2020 ◽

Vol 60 (9) ◽

pp. 1136

Author(s):

M. A. Nilforooshan

Keyword(s):

New Zealand ◽

Single Step ◽

Pedigree Information ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Genomic Relationship ◽

Genetic Trend ◽

Weaning Weight ◽

Sheep Population ◽

Pedigree Relationship

Context In New Zealand, Romney is the most predominant breed and is reared as a dual-purpose sheep. The number of genotypes is rapidly increasing in the sheep population, and making use of both genotypes and pedigree information is of importance for genetic evaluations. Single-step genomic best linear unbiased prediction (ssGBLUP) is a method for simultaneous prediction of genetic merits for genotyped and non-genotyped animals. The combination and the compatibility of the genomic relationship matrix (G) and the pedigree relationship matrix for genotyped animals (A22) is important for unbiased ssGBLUP. Aims The aim of the present study was to find an optimum genetic relationship matrix for ssGBLUP weaning-weight evaluation of Romney sheep in New Zealand. Methods Data consisted of adjusted weaning weights for 2422011 sheep, 50K single-nucleotide polymorphism genotypes for 13304 animals and 3028688 animals in the pedigree. Blending of G and A22 was tested with weights (k) ranging from 0.2 to 0.99 (kG + (1 – k)A22), followed by none or one of the three methods of tuning G to A22. Key results The averages of G and A22 were close to each other for overall, diagonal and off-diagonal elements. Therefore, differently tuned G performed similarly. However, elements of G showed larger variation than did the elements of A22 and, on average, genotyped animals were less related in G than in A22. Correlations between genomic estimated breeding values (GEBV) for the top 500 genotyped animals, as well as the rank correlations, were almost 1 among ssGBLUP evaluations using tuned G. The corresponding correlations with BLUP evaluations were increased by blending G with a larger proportion of A22, and were further increased by tuning G, indicating improved compatibility between G and A22. Blending and tuning G suppressed the inflation of GEBV and bias and it moved the genetic trend closer to the genetic trend obtained from BLUP. Conclusions A combination of blending and tuning G to A22, with a blending rate of 0.5 at most, is recommended for weaning weight of Romney sheep in New Zealand. Failure to do that resulted in inflated GEBV that can reduce the accuracy of selection, especially for genotyped animals. Implications There is a growing interest in the single-step GBLUP method for simultaneous genetic evaluation of genotyped and non-genotyped animals, in which genomic and pedigree relationship matrices are admixed. Using data from New Zealand Romney sheep, we have shown that adjustment of the genomic relationship matrix on the basis of the pedigree relationship matrix is necessary to avoid inflated evaluations. Improving the compatibility between genomic and pedigree relationship matrices is important for obtaining accurate and unbiased single-step GBLUP evaluations.

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Single-step prediction of genomic breeding value in a small dairy cattle population with strong import of foreign genes

Czech Journal of Animal Science ◽

10.17221/5890-cjas ◽

2012 ◽

Vol 57 (No. 4) ◽

pp. 151-159 ◽

Cited By ~ 7

Author(s):

J. Přibyl ◽

J. Haman ◽

T. Kott ◽

J. Přibylová ◽

M. Šimečková ◽

...

Keyword(s):

Single Step ◽

Allele Frequencies ◽

Breeding Value ◽

Progeny Testing ◽

Data Set ◽

Genomic Breeding ◽

Milk Performance ◽

Pedigree Relationship ◽

Foreign Genes ◽

Genomic Breeding Value

The breeding value (EBV) of Holstein cattle milk performance from the first lactation was evaluated using a regular Animal Model or by Single-Step Prediction of the genomic breeding value (GEBV). A total of 838 bulls were genotyped using the Illumina BovineSNP50 Beadchip V2. Two overlapping sets of milk performances were evaluated: calving years 1991–2004, with 729 341 lactations and 1 394 487 animals in the pedigree and calving years 1996–2009, with 808 436 lactations and 1 487 608 animals in the pedigree. The older data set included 526 genotyped bulls, in which the daughters’ milk performance was known for 210 individuals. All of the genotyped animals were included in the newer data set. Of the young genotyped bulls from the older set, 279 had more than 50 daughters with performance records in the newer set. Genomic relationship matrices (G) were constructed from the allele frequencies of the current genotyped population or by assuming a constant value of 0.5 for all loci. Using current allele frequencies, the correlation of G with the pedigree relationship (A) was 0.74, while it was 0.77 when the constant value was used. G was blended with A with weights of 80 or 99%. The average EBV of the genotyped bulls exceeded the mean EBV of the entire population by 3 SD. Although the number of reference bulls was small, genotyping resulted in an increase of approximately 0.05 in the correlation of the GEBV of young bulls with their results after progeny testing. Only small differences in correlations were found in dependency on the methods used for the determination of G and in dependency on the weight used in blending G with A. Both EBV and GEBV in the older set showed higher correlations with the GEBV of the newer set than the EBV of the newer set.  

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Integration of genomic information into beef cattle and sheep genetic evaluations in Australia

Animal Production Science ◽

10.1071/an11117 ◽

2012 ◽

Vol 52 (3) ◽

pp. 126 ◽

Cited By ~ 15

Author(s):

Andrew A. Swan ◽

David J. Johnston ◽

Daniel J. Brown ◽

Bruce Tier ◽

Hans-U. Graser

Keyword(s):

Beef Cattle ◽

Covariance Matrices ◽

Relationship Matrix ◽

Genomic Information ◽

Genetic Covariance ◽

Breeding Values ◽

Snp Panels ◽

Genetic Evaluations ◽

Genomic Predictions ◽

Cattle And Sheep

Genomic information has the potential to change the way beef cattle and sheep are selected and to substantially increase genetic gains. Ideally, genomic data will be used in combination with pedigree and phenotypic data to increase the accuracy of estimated breeding values (EBVs) and selection indexes. The first example of this in Australia was the integration of four markers for tenderness into beef cattle breeding values. Subsequently, the availability of high-density single nucleotide polymorphism (SNP) panels has made selection using genomic information possible, while at the same time creating significant challenges for genetic evaluation with regard to both data management and statistical modelling. Reference populations have been established in both the beef cattle and sheep industries, in which an extensive range of phenotypes have been collected and animals genotyped mainly using 50K SNP panels. From this information, genomic predictions of breeding value have been developed, albeit with varying levels of accuracy. These predictions have been incorporated into routine genetic evaluations using three approaches and trial results are now available to breeders. In the first, genomic predictions have been included in genetic evaluation models as additional traits. The challenges with this method have been the construction of consistent genetic covariance matrices, and a significant increase in computing time. The second approach has been to use a selection index procedure to blend genomic predictions with existing EBVs. This method has been shown to produce very similar results, and has the advantage of being simple to implement and fast to operate, although consistent genetic covariance matrices are still required. Third, in sheep a single-step analysis combining a genomic relationship matrix with a standard pedigree-based relationship matrix has been used to estimate breeding values for carcass and eating-quality traits. It is likely that this procedure or one similar will be incorporated into routine evaluations in the near future. While significant progress has been made in implementing methods of integrating genomic information in both beef and sheep evaluations in Australia, the major challenges for the future will be to continue to collect the phenotypes needed to derive accurate genomic predictions, and in managing much larger volumes of genomic data as the number of animals genotyped and the density of markers increase.

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Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd

Animals ◽

10.3390/ani11010024 ◽

2020 ◽

Vol 11 (1) ◽

pp. 24

Author(s):

Megan Scholtens ◽

Nicolas Lopez-Villalobos ◽

Klaus Lehnert ◽

Russell Snell ◽

Dorian Garrick ◽

...

Keyword(s):

New Zealand ◽

Somatic Cell ◽

Milk Fat ◽

Single Step ◽

Somatic Cell Score ◽

Dairy Goat ◽

Nucleotide Polymorphisms ◽

Genomic Information ◽

Breeding Values ◽

Goat Population

Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.

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A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study

Frontiers in Genetics ◽

10.3389/fgene.2021.729867 ◽

2021 ◽

Vol 12 ◽

Author(s):

Andre C. Araujo ◽

Paulo L. S. Carneiro ◽

Hinayah R. Oliveira ◽

Flavio S. Schenkel ◽

Renata Veroneze ◽

...

Keyword(s):

Genetic Diversity ◽

Predictive Ability ◽

Single Step ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Diverse Populations ◽

Nucleotide Polymorphisms ◽

Unique Haplotype ◽

Individual Snps ◽

Genomic Predictions

The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix (G) in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second G (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne ≅ 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne ≅ 250) and less (Ne ≅ 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two G matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

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Genetic and genomic characterization followed by single-step genomic evaluation of withers height in German Warmblood horses

Journal of Applied Genetics ◽

10.1007/s13353-021-00681-w ◽

2022 ◽

Author(s):

Sarah Vosgerau ◽

Nina Krattenmacher ◽

Clemens Falker-Gieske ◽

Anita Seidel ◽

Jens Tetens ◽

...

Keyword(s):

Genome Wide Association Study ◽

Reference Population ◽

Single Step ◽

Relationship Matrix ◽

Nucleotide Polymorphisms ◽

Step Method ◽

Genomic Breeding ◽

Breeding Values ◽

A Genome ◽

Estimation System

Abstract Reliability of genomic predictions is influenced by the size and genetic composition of the reference population. For German Warmblood horses, compilation of a reference population has been enabled through the cooperation of five German breeding associations. In this study, preliminary data from this joint reference population were used to genetically and genomically characterize withers height and to apply single-step methodology for estimating genomic breeding values for withers height. Using data on 2113 mares and their genomic information considering about 62,000 single nucleotide polymorphisms (SNPs), analysis of the genomic relationship revealed substructures reflecting breed origin and different breeding goals of the contributing breeding associations. A genome-wide association study confirmed a known quantitative trait locus (QTL) for withers height on equine chromosome (ECA) 3 close to LCORL and identified a further significant peak on ECA 1. Using a single-step approach with a combined relationship matrix, the estimated heritability for withers height was 0.31 (SE = 0.08) and the corresponding genomic breeding values ranged from − 2.94 to 2.96 cm. A mean reliability of 0.38 was realized for these breeding values. The analyses of withers height showed that compiling a reference population across breeds is a suitable strategy for German Warmblood horses. The single-step method is an appealing approach for practical genomic prediction in horses, because not many genotypes are available yet and animals without genotypes can by this way directly contribute to the estimation system.

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